rq_rag_llama2_7B
rq_rag_llama2_7B is a 7 billion parameter chat model from chiminchan, released April 26, 2024. rq_rag_llama2_7B is an open-weights chat model with roughly 7 billion parameters.
by chiminchan · 7B parameters
Best for
Ways to use rq_rag_llama2_7B in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your chiminchan API key. osFoundry discovers rq_rag_llama2_7B automatically — assign it to a Maestro role (router, direct, orchestrator, or fallback) in the Pipeline tab and it is live in every chat. Your key, your provider account — no token markup.
Deploy a dedicated endpoint
rq_rag_llama2_7B is open-weights — run it locally for free, or deploy a dedicated GPU endpoint in your workspace for reserved capacity with no rate limits.
Use it in a Room App
Room Apps declare AI features in their manifest, then call them with invokeAI:
import { invokeAI } from '@osfoundry/app-sdk'
// 'summarize' is an AI feature declared in your app manifest.
const result = await invokeAI('summarize', userText)
Call it from your own apps
Once a model is wired into your workspace you can host it as an API and reach it from your own services, scripts, or CI — outside osFoundry.
What hardware can run rq_rag_llama2_7B
rq_rag_llama2_7B runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~17 GB).
rq_rag_llama2_7B vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about rq_rag_llama2_7B
Is rq_rag_llama2_7B free to use?
rq_rag_llama2_7B is free to run locally on your own hardware. Hosted access through osFoundry is metered (input Free (local), output Free (local)). You can switch between local and hosted at any time.
Can I use rq_rag_llama2_7B commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
How much VRAM does rq_rag_llama2_7B need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run rq_rag_llama2_7B locally?
Yes. rq_rag_llama2_7B is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is rq_rag_llama2_7B best at?
rq_rag_llama2_7B is well-suited to text generation.
How do I use rq_rag_llama2_7B in osFoundry?
Paste your chiminchan API key in the key dialog (or deploy the open weights for self-hostable models), assign rq_rag_llama2_7B to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by chiminchan on April 26, 2024. Source: https://huggingface.co/chiminchan/rq_rag_llama2_7B